Sign Up for a free trial: https://rampedup.io/sign-up-%2F-log-in - 7 Days and 50 Credits to test our quality and accuracy.

These are the fields available within the RampedUp Global dataset.

CONTACT DATA: Personal Email Address - We manage over 115 million personal email addresses Professional Email - We manage over 200 million professional email addresses Home Address - We manage over 20 million home addresses Mobile Phones - 65 million direct lines to decision makers Social Profiles - Individual Facebook, Twitter, and LinkedIn Local Address - We manage 65M locations for local office mailers, event-based marketing or face-to-face sales calls.

JOB DATA: Job Title - Standardized titles for ease of use and selection Company Name - The Contact's current employer Job Function - The Company Department associated with the job role Title Level - The Level in the Company associated with the job role Job Start Date - Identify people new to their role as a potential buyer

EMPLOYER DATA: Websites - Company Website, Root Domain, or Full Domain Addresses - Standardized Address, City, Region, Postal Code, and Country Phone - E164 phone with country code Social Profiles - LinkedIn, CrunchBase, Facebook, and Twitter

FIRMOGRAPHIC DATA: Industry - 420 classifications for categorizing the company’s main field of business Sector - 20 classifications for categorizing company industries 4 Digit SIC Code - 239 classifications and their definitions 6 Digit NAICS - 452 classifications and their definitions Revenue - Estimated revenue and bands from 1M to over 1B Employee Size - Exact employee count and bands Email Open Scores - Aggregated data at the domain level showing relationships between email opens and corporate domains. IP Address -Company level IP Addresses associated to Domains from a DNS lookup

CONSUMER DATA: Education - Alma Mater, Degree, Graduation Date Skills - Accumulated Skills associated with work experience
Interests - Known interests of contact Connections - Number of social connections. Followers - Number of social followers

Download our data dictionary: https://rampedup.io/our-data

The global number of LinkedIn users in was forecast to continuously increase between 2024 and 2028 by in total 171.9 million users (+22.3 percent). After the sixth consecutive increasing year, the LinkedIn user base is estimated to reach 942.84 million users and therefore a new peak in 2028. User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of LinkedIn users in countries like Asia and South America.

Full profile of 10,000 Swedish companies - download here, data schema here, with more than 40 data points including - Company name - Website - Location - Industry and many more!

There are additionally millions more companies profiles available, visit the LinkDB product page here.

Our LinkDB database is an exhaustive database of publicly accessible LinkedIn people and companies profiles. It contains close to 500 Million people and companies profiles globally.

The number of LinkedIn users in the United States was forecast to continuously increase between 2024 and 2028 by in total 10.4 million users (+5.23 percent). After the ninth consecutive increasing year, the LinkedIn user base is estimated to reach 209.26 million users and therefore a new peak in 2028. Notably, the number of LinkedIn users of was continuously increasing over the past years.User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of LinkedIn users in countries like Canada and Mexico.

Allforce is a leading data intelligence company specializing in comprehensive audience targeting solutions. We maintain one of the most extensive and accurate databases of professional contact information, with a focus on delivering verified, actionable data that drives measurable marketing results for our clients.

Dataset Overview: Our US Human Resources Professional Contact Database provides access to 2.4 million verified HR professionals across 475,000 companies nationwide. This premium dataset is specifically curated for B2B marketers seeking to connect with decision-makers in the HR ecosystem.

Key Features & Benefits: 2.4M+ HR professionals across all specialties 475,000+ companies represented Segmented by HR function: Benefits, Payroll, Recruiting, Training, Compensation, and more Decision-maker level contacts included

Data Quality & Verification: LinkedIn URL verification for each contact Regular database updates and maintenance High deliverability rates (Email Safe certification) Active professional verification process

Multi-Channel Marketing Support: Email addresses (newsletter-safe, verified deliverable) Direct phone numbers for telemarketing Postal addresses for direct mail campaigns LinkedIn profile matching for social outreach Digital advertising - Programmatic audiences

Data Compliance & Safety: All data is collected and maintained in compliance with applicable privacy regulations. Our "Safe to Email" certification ensures subscribers have opted into professional communications, reducing bounce rates and compliance risks.

Industries Served: Healthcare, Technology, Manufacturing, Financial Services, Retail, Education, Government, and all major industry verticals with HR departments.

Transform your HR marketing strategy with verified, actionable contact data that delivers results.

EXIOBASE 3: For best in class environmental-economic accounting data. Get insight into global supply-chains and the environmental impacts of consumption.

EXIOBASE 3 provides a time series of environmentally extended multi-regional input‐output (EE MRIO) tables ranging from 1995 to 2020 (plus now-casted tables for 2021 and 2022) for 44 countries (27 EU member plus 17 major economies) and five rest of the world regions.

EXIOBASE is maintained by the EXIOBASE consortium, with XIO Sustainability Analytics now working on providing annual updates to the core economic, energy and emission tables. We welcome any collaborative efforts to further improve the database.

Updates are now being produced annually, and more updated data may be available in beta-mode, get in contact if interested. At time of publication of v3.9.4, a version 3.10 with updates to 2022 and nowcasts to 2024 is in beta.

A special issue of Journal of Industrial Ecology (Volume 22, Issue 3) describes the build process and some use cases of EXIOBASE 3. This includes the article by Stadler et al. (2018) describing the compilation of EXIOBASE 3.

To stay updated on database improvements, relevant EXIOBASE studies, and ongoing work, join the EXIOBASE group on LinkedIn.

Licenses

Please ensure that you have understood the license conditions before use. Note that these conditions are significantly different to the license conditions of earlier versions, such as v3.8.

Non-commercial, academic useEXIOBASE v3.9 is released under a customized derivative of the CC-BY-SA-NC license, incorporating additional definitions as outlined in the license file.

Commercial useCommercial licenses, which allow for use for any case not covered in the non-commercial license are under development. For license enquiries or help in use of EXIOBASE data for spend-based emission factors, or other applications, please send an email.

The funding to be accumulated through licenses and support will be used to fund further updates of the database.

Now-casting

The core EXIOBASE 3.9 model is based on supply and use tables up to 2020. However, the time-series is expanded (i.e., now-casted) until 2022 using global trade data and macroeconomic data (IMF), as well as environmental data when available. Caution should be made when using now-casted data, especially due to the impact of the COVID pandemic not being adequately captured in the now-casting. It is recommended to use 2020 data from v3.9.4 as the latest available year for most analysis.

Processing the database

For a general introduction to environmentally extended input-output modelling, we refer to:

UN Handbook on Supply and Use Tables and Input Output-Tables with Extensions and Applications

Input-Output Analysis by Miller & Blair

The database is too large to handle in a standard spreadsheet software (e.g., Excel), and we recommend using programming languages such as Python, R, or Matlab. The open-source python package PyMRIO can be used to download and parse the database directly from Zenodo and do input-output analysis.

If you are interested in learning more about EXIOBASE or input-output modelling in general (including practical use of PyMRIO, how to develop custom models), please reach out.

Earlier versions and documentation

Some previous versions (3.7, 3.8) are also available on Zenodo. The even earlier public releases of the data (EXIOBASE v3.3 and v3.4) are available on request. We recommend, however, using the latest version due to significant updates of the economic data as well as major differences in water and land use accounts.

The first documentation of EXIOBASE 3 was done via deliverables of the DESIRE project - these can now be accessed here.

The country disaggregated version, EXIOBASE 3rx, is available on Zenodo. It is no longer continued, but including more regions in the EXIOBASE classification is ongoing work. Reach out to exiobase-support@googlegroups.com, if interested in collaboration on integrating specific countries.

Future Updates and Announcements

Updates are now being produced annually, and a beta version of 3.10 is already under development, extending most data to 2022. To stay updated, join the EXIOBASE group on LinkedIn and/or reach out to exiobase-support@googlegroups.com.

The number of LinkedIn users in Africa was forecast to continuously increase between 2024 and 2028 by in total 37 million users (+68.13 percent). After the ninth consecutive increasing year, the LinkedIn user base is estimated to reach 91.29 million users and therefore a new peak in 2028. Notably, the number of LinkedIn users of was continuously increasing over the past years.User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of LinkedIn users in countries like South America and Caribbean.

Graph and download economic data for Software Development Job Postings on Indeed in the United States (IHLIDXUSTPSOFTDEVE) from 2020-02-01 to 2025-06-13 about software, jobs, and USA.

This is the dataset for the Axisymmetric body (0.16m diameter, elliptical nose cone, l/d = 5), collated by Max Varney (https://www.linkedin.com/in/max-varney/) on 2020/06/15. More information for the geometry and analysis using dataset #1 can be found in the paper "Three dimensional structure of the unsteady wake of an axisymmetric body", Physics of Fluids 31, 025113 (2019); https://doi.org/10.1063/1.5078379. The paper is also available on the Loughbrough University research repository at: https://hdl.handle.net/2134/37058Data was collected in the Large Wind Tunnel at Loughborough University, a 2.5m^2, closed working section, fixed ground open return tunnel. Details of the tunnel can be found in: https://hdl.handle.net/2134/6674The CAD geometry for the wind tunnel and the axisymmetric body with its mounting are included in the dataset as ASCII .stl files, with the units in meters. Note: at the start of every experiment the yaw and pitch of the model was incrementally adjusted to produce a symmetric base pressure.All data is presented in SI units and all measurements are from the origin of the model (on the base of the model, at the centre of the base) with x positive downstream and z positive up, using a right hand rule to find positive y.There are two datasets. DATASET #1 - 30m/s (Re_d=3.2x10^5)Contains: Tomographic Particle Image Velocimetry and Base PressuresDATASET #2 - 40m/s (Re_d=4.3x10^5)Contains: Planar (Y=0m, Z=0m and Z=0.04m) and Stereo (X=0.06m, X=0.12m, X=0.18m and X=0.24m) Particle Image Velocimetry, Base Pressures and Forces

Water and Sanitation Mapping, Malawi conducted with Crowddroning by GLOBHE. More maps and data available on demand upon request from locations globally.

Full GeoTIF file available for download on request. Contact globhe@globhe.com for download link.

To request drone data on demand at global scale make your request at: https://globhe.com/drone-data-request

MORE CROWDDRONING BY GLOBHE

Webb: https://globhe.com/

Facebook: https://www.facebook.com/Crowddroning

Twitter: https://twitter.com/globhedrones

Instagram: https://www.instagram.com/globhedrones/

LinkedIn: https://www.linkedin.com/company/globhedrones/

ABSTRACT In this project, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the proposed testbed is organized into seven layers, including, Cloud Computing Layer, Network Functions Virtualization Layer, Blockchain Network Layer, Fog Computing Layer, Software-Defined Networking Layer, Edge Computing Layer, and IoT and IIoT Perception Layer. In each layer, we propose new emerging technologies that satisfy the key requirements of IoT and IIoT applications, such as, ThingsBoard IoT platform, OPNFV platform, Hyperledger Sawtooth, Digital twin, ONOS SDN controller, Mosquitto MQTT brokers, Modbus TCP/IP, ...etc. The IoT data are generated from various IoT devices (more than 10 types) such as Low-cost digital sensors for sensing temperature and humidity, Ultrasonic sensor, Water level detection sensor, pH Sensor Meter, Soil Moisture sensor, Heart Rate Sensor, Flame Sensor, ...etc.). However, we identify and analyze fourteen attacks related to IoT and IIoT connectivity protocols, which are categorized into five threats, including, DoS/DDoS attacks, Information gathering, Man in the middle attacks, Injection attacks, and Malware attacks. In addition, we extract features obtained from different sources, including alerts, system resources, logs, network traffic, and propose new 61 features with high correlations from 1176 found features. After processing and analyzing the proposed realistic cyber security dataset, we provide a primary exploratory data analysis and evaluate the performance of machine learning approaches (i.e., traditional machine learning as well as deep learning) in both centralized and federated learning modes.

Instructions:

Great news! The Edge-IIoT dataset has been featured as a "Document in the top 1% of Web of Science." This indicates that it is ranked within the top 1% of all publications indexed by the Web of Science (WoS) in terms of citations and impact.

Please kindly visit kaggle link for the updates: https://www.kaggle.com/datasets/mohamedamineferrag/edgeiiotset-cyber-sec...

Free use of the Edge-IIoTset dataset for academic research purposes is hereby granted in perpetuity. Use for commercial purposes is allowable after asking the leader author, Dr Mohamed Amine Ferrag, who has asserted his right under the Copyright.

The details of the Edge-IIoT dataset were published in following the paper. For the academic/public use of these datasets, the authors have to cities the following paper:

Mohamed Amine Ferrag, Othmane Friha, Djallel Hamouda, Leandros Maglaras, Helge Janicke, "Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning", IEEE Access, April 2022 (IF: 3.37), DOI: 10.1109/ACCESS.2022.3165809

Link to paper : https://ieeexplore.ieee.org/document/9751703

The directories of the Edge-IIoTset dataset include the following:

•File 1 (Normal traffic)

-File 1.1 (Distance): This file includes two documents, namely, Distance.csv and Distance.pcap. The IoT sensor (Ultrasonic sensor) is used to capture the IoT data.

-File 1.2 (Flame_Sensor): This file includes two documents, namely, Flame_Sensor.csv and Flame_Sensor.pcap. The IoT sensor (Flame Sensor) is used to capture the IoT data.

-File 1.3 (Heart_Rate): This file includes two documents, namely, Flame_Sensor.csv and Flame_Sensor.pcap. The IoT sensor (Flame Sensor) is used to capture the IoT data.

-File 1.4 (IR_Receiver): This file includes two documents, namely, IR_Receiver.csv and IR_Receiver.pcap. The IoT sensor (IR (Infrared) Receiver Sensor) is used to capture the IoT data.

-File 1.5 (Modbus): This file includes two documents, namely, Modbus.csv and Modbus.pcap. The IoT sensor (Modbus Sensor) is used to capture the IoT data.

-File 1.6 (phValue): This file includes two documents, namely, phValue.csv and phValue.pcap. The IoT sensor (pH-sensor PH-4502C) is used to capture the IoT data.

-File 1.7 (Soil_Moisture): This file includes two documents, namely, Soil_Moisture.csv and Soil_Moisture.pcap. The IoT sensor (Soil Moisture Sensor v1.2) is used to capture the IoT data.

-File 1.8 (Sound_Sensor): This file includes two documents, namely, Sound_Sensor.csv and Sound_Sensor.pcap. The IoT sensor (LM393 Sound Detection Sensor) is used to capture the IoT data.

-File 1.9 (Temperature_and_Humidity): This file includes two documents, namely, Temperature_and_Humidity.csv and Temperature_and_Humidity.pcap. The IoT sensor (DHT11 Sensor) is used to capture the IoT data.

-File 1.10 (Water_Level): This file includes two documents, namely, Water_Level.csv and Water_Level.pcap. The IoT sensor (Water sensor) is used to capture the IoT data.

•File 2 (Attack traffic):

-File 2.1 (Attack traffic (CSV files)): This file includes 13 documents, namely, Backdoor_attack.csv, DDoS_HTTP_Flood_attack.csv, DDoS_ICMP_Flood_attack.csv, DDoS_TCP_SYN_Flood_attack.csv, DDoS_UDP_Flood_attack.csv, MITM_attack.csv, OS_Fingerprinting_attack.csv, Password_attack.csv, Port_Scanning_attack.csv, Ransomware_attack.csv, SQL_injection_attack.csv, Uploading_attack.csv, Vulnerability_scanner_attack.csv, XSS_attack.csv. Each document is specific for each attack.

-File 2.2 (Attack traffic (PCAP files)): This file includes 13 documents, namely, Backdoor_attack.pcap, DDoS_HTTP_Flood_attack.pcap, DDoS_ICMP_Flood_attack.pcap, DDoS_TCP_SYN_Flood_attack.pcap, DDoS_UDP_Flood_attack.pcap, MITM_attack.pcap, OS_Fingerprinting_attack.pcap, Password_attack.pcap, Port_Scanning_attack.pcap, Ransomware_attack.pcap, SQL_injection_attack.pcap, Uploading_attack.pcap, Vulnerability_scanner_attack.pcap, XSS_attack.pcap. Each document is specific for each attack.

•File 3 (Selected dataset for ML and DL):

-File 3.1 (DNN-EdgeIIoT-dataset): This file contains a selected dataset for the use of evaluating deep learning-based intrusion detection systems.

-File 3.2 (ML-EdgeIIoT-dataset): This file contains a selected dataset for the use of evaluating traditional machine learning-based intrusion detection systems.

Step 1: Downloading The Edge-IIoTset dataset From the Kaggle platform from google.colab import files

!pip install -q kaggle

files.upload()

!mkdir ~/.kaggle

!cp kaggle.json ~/.kaggle/

!chmod 600 ~/.kaggle/kaggle.json

!kaggle datasets download -d mohamedamineferrag/edgeiiotset-cyber-security-dataset-of-iot-iiot -f "Edge-IIoTset dataset/Selected dataset for ML and DL/DNN-EdgeIIoT-dataset.csv"

!unzip DNN-EdgeIIoT-dataset.csv.zip

!rm DNN-EdgeIIoT-dataset.csv.zip

Step 2: Reading the Datasets' CSV file to a Pandas DataFrame: import pandas as pd

import numpy as np

df = pd.read_csv('DNN-EdgeIIoT-dataset.csv', low_memory=False)

Step 3 : Exploring some of the DataFrame's contents: df.head(5)

print(df['Attack_type'].value_counts())

Step 4: Dropping data (Columns, duplicated rows, NAN, Null..): from sklearn.utils import shuffle

drop_columns = ["frame.time", "ip.src_host", "ip.dst_host", "arp.src.proto_ipv4","arp.dst.proto_ipv4",

 "http.file_data","http.request.full_uri","icmp.transmit_timestamp",

 "http.request.uri.query", "tcp.options","tcp.payload","tcp.srcport",

 "tcp.dstport", "udp.port", "mqtt.msg"]

df.drop(drop_columns, axis=1, inplace=True)

df.dropna(axis=0, how='any', inplace=True)

df.drop_duplicates(subset=None, keep="first", inplace=True)

df = shuffle(df)

df.isna().sum()

print(df['Attack_type'].value_counts())

Step 5: Categorical data encoding (Dummy Encoding): import numpy as np

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

from sklearn import preprocessing

def encode_text_dummy(df, name):

dummies = pd.get_dummies(df[name])

for x in dummies.columns:

dummy_name = f"{name}-{x}"

df[dummy_name] = dummies[x]

df.drop(name, axis=1, inplace=True)

encode_text_dummy(df,'http.request.method')

encode_text_dummy(df,'http.referer')

encode_text_dummy(df,"http.request.version")

encode_text_dummy(df,"dns.qry.name.len")

encode_text_dummy(df,"mqtt.conack.flags")

encode_text_dummy(df,"mqtt.protoname")

encode_text_dummy(df,"mqtt.topic")

Step 6: Creation of the preprocessed dataset df.to_csv('preprocessed_DNN.csv', encoding='utf-8')

For more information about the dataset, please contact the lead author of this project, Dr Mohamed Amine Ferrag, on his email: mohamed.amine.ferrag@gmail.com

More information about Dr. Mohamed Amine Ferrag is available at:

https://www.linkedin.com/in/Mohamed-Amine-Ferrag

https://dblp.uni-trier.de/pid/142/9937.html

https://www.researchgate.net/profile/Mohamed_Amine_Ferrag

https://scholar.google.fr/citations?user=IkPeqxMAAAAJ&hl=fr&oi=ao

https://www.scopus.com/authid/detail.uri?authorId=56115001200

https://publons.com/researcher/1322865/mohamed-amine-ferrag/

https://orcid.org/0000-0002-0632-3172

Last Updated: 27 Mar. 2023

This statistic shows a ranking of the estimated number of LinkedIn users in 2020 in Africa, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

The number of LinkedIn users in Ghana was forecast to continuously increase between 2024 and 2028 by in total 0.3 million users (+10.6 percent). After the eighth consecutive increasing year, the LinkedIn user base is estimated to reach 3.14 million users and therefore a new peak in 2028. User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of LinkedIn users in countries like Ivory Coast and Nigeria.

The number of LinkedIn users in South Africa was forecast to continuously increase between 2024 and 2028 by in total 2.7 million users (+28.75 percent). After the eighth consecutive increasing year, the LinkedIn user base is estimated to reach 12.04 million users and therefore a new peak in 2028. User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of LinkedIn users in countries like Lesotho and Botswana.

The number of LinkedIn users in Australia was forecast to continuously increase between 2024 and 2028 by in total 0.5 million users (+3.74 percent). After the ninth consecutive increasing year, the LinkedIn user base is estimated to reach 13.89 million users and therefore a new peak in 2028. Notably, the number of LinkedIn users of was continuously increasing over the past years.User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of LinkedIn users in countries like Fiji and New Zealand.